Overview

Dataset statistics

Number of variables34
Number of observations1200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory318.9 KiB
Average record size in memory272.1 B

Variable types

Numeric15
Categorical17
Boolean2

Alerts

Over18 has constant value "True" Constant
StandardHours has constant value "80" Constant
YearsAtCompany is highly correlated with YearsInCurrentRoleHigh correlation
YearsInCurrentRole is highly correlated with YearsAtCompanyHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
YearsAtCompany is highly correlated with YearsInCurrentRoleHigh correlation
YearsInCurrentRole is highly correlated with YearsAtCompanyHigh correlation
BusinessTravel is highly correlated with Over18 and 1 other fieldsHigh correlation
Gender is highly correlated with Over18 and 1 other fieldsHigh correlation
Attrition is highly correlated with Over18 and 1 other fieldsHigh correlation
EducationField is highly correlated with Over18 and 1 other fieldsHigh correlation
Over18 is highly correlated with BusinessTravel and 17 other fieldsHigh correlation
MaritalStatus is highly correlated with Over18 and 1 other fieldsHigh correlation
StockOptionLevel is highly correlated with Over18 and 1 other fieldsHigh correlation
Department is highly correlated with Over18 and 2 other fieldsHigh correlation
StandardHours is highly correlated with BusinessTravel and 17 other fieldsHigh correlation
EnvironmentSatisfaction is highly correlated with Over18 and 1 other fieldsHigh correlation
JobSatisfaction is highly correlated with Over18 and 1 other fieldsHigh correlation
JobRole is highly correlated with Over18 and 2 other fieldsHigh correlation
JobInvolvement is highly correlated with Over18 and 1 other fieldsHigh correlation
PerformanceRating is highly correlated with Over18 and 1 other fieldsHigh correlation
OverTime is highly correlated with Over18 and 1 other fieldsHigh correlation
Education is highly correlated with Over18 and 1 other fieldsHigh correlation
WorkLifeBalance is highly correlated with Over18 and 1 other fieldsHigh correlation
JobLevel is highly correlated with Over18 and 1 other fieldsHigh correlation
RelationshipSatisfaction is highly correlated with Over18 and 1 other fieldsHigh correlation
Department is highly correlated with EducationField and 1 other fieldsHigh correlation
EducationField is highly correlated with DepartmentHigh correlation
JobLevel is highly correlated with JobRole and 1 other fieldsHigh correlation
JobRole is highly correlated with Department and 1 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly correlated with JobLevelHigh correlation
YearsAtCompany is highly correlated with YearsInCurrentRole and 2 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with YearsAtCompany and 2 other fieldsHigh correlation
id has unique values Unique
DailyRate has unique values Unique
DistanceFromHome has unique values Unique
NumCompaniesWorked has 167 (13.9%) zeros Zeros
TrainingTimesLastYear has 53 (4.4%) zeros Zeros
YearsAtCompany has 23 (1.9%) zeros Zeros
YearsInCurrentRole has 208 (17.3%) zeros Zeros
YearsSinceLastPromotion has 499 (41.6%) zeros Zeros
YearsWithCurrManager has 239 (19.9%) zeros Zeros

Reproduction

Analysis started2022-07-31 02:09:26.064589
Analysis finished2022-07-31 02:10:04.918820
Duration38.85 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

UNIQUE

Distinct1200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1013.075833
Minimum0
Maximum1998
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:05.033068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile108.85
Q1508.75
median1018
Q31519.25
95-th percentile1907.05
Maximum1998
Range1998
Interquartile range (IQR)1010.5

Descriptive statistics

Standard deviation575.7260362
Coefficient of variation (CV)0.5682951042
Kurtosis-1.202750413
Mean1013.075833
Median Absolute Deviation (MAD)506
Skewness-0.02497263307
Sum1215691
Variance331460.4688
MonotonicityStrictly increasing
2022-07-31T02:10:05.156673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
13651
 
0.1%
13601
 
0.1%
13541
 
0.1%
13531
 
0.1%
13521
 
0.1%
13511
 
0.1%
13501
 
0.1%
13491
 
0.1%
13481
 
0.1%
Other values (1190)1190
99.2%
ValueCountFrequency (%)
01
0.1%
31
0.1%
71
0.1%
101
0.1%
111
0.1%
121
0.1%
151
0.1%
201
0.1%
211
0.1%
231
0.1%
ValueCountFrequency (%)
19981
0.1%
19971
0.1%
19961
0.1%
19941
0.1%
19881
0.1%
19871
0.1%
19861
0.1%
19841
0.1%
19801
0.1%
19761
0.1%

Age
Real number (ℝ≥0)

Distinct36
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.70166667
Minimum17
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:05.272920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile18
Q126
median34
Q337
95-th percentile47
Maximum56
Range39
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.28219936
Coefficient of variation (CV)0.2532653594
Kurtosis-0.5190491203
Mean32.70166667
Median Absolute Deviation (MAD)7
Skewness0.06648078236
Sum39242
Variance68.59482624
MonotonicityNot monotonic
2022-07-31T02:10:05.365272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
37221
18.4%
26159
13.2%
34146
12.2%
25125
10.4%
3567
 
5.6%
2759
 
4.9%
1750
 
4.2%
4648
 
4.0%
1846
 
3.8%
4539
 
3.2%
Other values (26)240
20.0%
ValueCountFrequency (%)
1750
 
4.2%
1846
 
3.8%
196
 
0.5%
203
 
0.2%
222
 
0.2%
25125
10.4%
26159
13.2%
2759
 
4.9%
2820
 
1.7%
2912
 
1.0%
ValueCountFrequency (%)
561
 
0.1%
551
 
0.1%
533
 
0.2%
523
 
0.2%
512
 
0.2%
507
 
0.6%
497
 
0.6%
4816
 
1.3%
4728
2.3%
4648
4.0%

BusinessTravel
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Travel_Rarely
808 
Travel_Frequently
263 
Non-Travel
129 

Length

Max length17
Median length13
Mean length13.55416667
Min length10

Characters and Unicode

Total characters16265
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Rarely
4th rowTravel_Rarely
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely808
67.3%
Travel_Frequently263
 
21.9%
Non-Travel129
 
10.8%

Length

2022-07-31T02:10:05.464923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:05.571053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely808
67.3%
travel_frequently263
 
21.9%
non-travel129
 
10.8%

Most occurring characters

ValueCountFrequency (%)
e2534
15.6%
r2271
14.0%
l2271
14.0%
a2008
12.3%
T1200
7.4%
v1200
7.4%
y1071
6.6%
_1071
6.6%
R808
 
5.0%
n392
 
2.4%
Other values (7)1439
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12665
77.9%
Uppercase Letter2400
 
14.8%
Connector Punctuation1071
 
6.6%
Dash Punctuation129
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2534
20.0%
r2271
17.9%
l2271
17.9%
a2008
15.9%
v1200
9.5%
y1071
8.5%
n392
 
3.1%
q263
 
2.1%
u263
 
2.1%
t263
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
T1200
50.0%
R808
33.7%
F263
 
11.0%
N129
 
5.4%
Connector Punctuation
ValueCountFrequency (%)
_1071
100.0%
Dash Punctuation
ValueCountFrequency (%)
-129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15065
92.6%
Common1200
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2534
16.8%
r2271
15.1%
l2271
15.1%
a2008
13.3%
T1200
8.0%
v1200
8.0%
y1071
7.1%
R808
 
5.4%
n392
 
2.6%
F263
 
1.7%
Other values (5)1047
6.9%
Common
ValueCountFrequency (%)
_1071
89.2%
-129
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII16265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2534
15.6%
r2271
14.0%
l2271
14.0%
a2008
12.3%
T1200
7.4%
v1200
7.4%
y1071
6.6%
_1071
6.6%
R808
 
5.0%
n392
 
2.4%
Other values (7)1439
8.8%

DailyRate
Real number (ℝ≥0)

UNIQUE

Distinct1200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean750.7541568
Minimum59.23158071
Maximum1484.979305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:05.663445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum59.23158071
5-th percentile83.84835369
Q1403.9460427
median736.8734628
Q31092.443495
95-th percentile1365.123747
Maximum1484.979305
Range1425.747724
Interquartile range (IQR)688.4974523

Descriptive statistics

Standard deviation415.4209173
Coefficient of variation (CV)0.5533381514
Kurtosis-1.178988572
Mean750.7541568
Median Absolute Deviation (MAD)339.7413504
Skewness-0.06742704441
Sum900904.9881
Variance172574.5385
MonotonicityNot monotonic
2022-07-31T02:10:05.808391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450.94147611
 
0.1%
1233.3371111
 
0.1%
664.25263871
 
0.1%
457.50173841
 
0.1%
1256.2364341
 
0.1%
1015.3687741
 
0.1%
65.945670251
 
0.1%
992.82582371
 
0.1%
360.69025881
 
0.1%
677.28196961
 
0.1%
Other values (1190)1190
99.2%
ValueCountFrequency (%)
59.231580711
0.1%
59.516645811
0.1%
59.59566161
0.1%
60.158413981
0.1%
61.271888981
0.1%
61.667846521
0.1%
61.7732761
0.1%
61.965076561
0.1%
62.14327041
0.1%
63.100392121
0.1%
ValueCountFrequency (%)
1484.9793051
0.1%
1474.7331891
0.1%
1474.0572421
0.1%
1469.3184861
0.1%
1462.7414511
0.1%
1460.2816851
0.1%
1458.5153271
0.1%
1456.7249871
0.1%
1456.4891881
0.1%
1445.3565371
0.1%

Department
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Research & Development
773 
Sales
381 
Human Resources
 
46

Length

Max length22
Median length22
Mean length16.33416667
Min length5

Characters and Unicode

Total characters19601
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch & Development
2nd rowResearch & Development
3rd rowHuman Resources
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development773
64.4%
Sales381
31.8%
Human Resources46
 
3.8%

Length

2022-07-31T02:10:06.124708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:06.234793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
research773
27.7%
773
27.7%
development773
27.7%
sales381
13.6%
human46
 
1.6%
resources46
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e4338
22.1%
1592
 
8.1%
s1246
 
6.4%
a1200
 
6.1%
l1154
 
5.9%
R819
 
4.2%
r819
 
4.2%
c819
 
4.2%
n819
 
4.2%
m819
 
4.2%
Other values (10)5976
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15217
77.6%
Uppercase Letter2019
 
10.3%
Space Separator1592
 
8.1%
Other Punctuation773
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4338
28.5%
s1246
 
8.2%
a1200
 
7.9%
l1154
 
7.6%
r819
 
5.4%
c819
 
5.4%
n819
 
5.4%
m819
 
5.4%
o819
 
5.4%
p773
 
5.1%
Other values (4)2411
15.8%
Uppercase Letter
ValueCountFrequency (%)
R819
40.6%
D773
38.3%
S381
18.9%
H46
 
2.3%
Space Separator
ValueCountFrequency (%)
1592
100.0%
Other Punctuation
ValueCountFrequency (%)
&773
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17236
87.9%
Common2365
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4338
25.2%
s1246
 
7.2%
a1200
 
7.0%
l1154
 
6.7%
R819
 
4.8%
r819
 
4.8%
c819
 
4.8%
n819
 
4.8%
m819
 
4.8%
o819
 
4.8%
Other values (8)4384
25.4%
Common
ValueCountFrequency (%)
1592
67.3%
&773
32.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII19601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4338
22.1%
1592
 
8.1%
s1246
 
6.4%
a1200
 
6.1%
l1154
 
5.9%
R819
 
4.2%
r819
 
4.2%
c819
 
4.2%
n819
 
4.2%
m819
 
4.2%
Other values (10)5976
30.5%

DistanceFromHome
Real number (ℝ)

UNIQUE

Distinct1200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.21838691
Minimum-0.023998944
Maximum29.89020821
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size9.5 KiB
2022-07-31T02:10:06.390007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.023998944
5-th percentile0.9204483909
Q12.688172871
median9.345923648
Q315.23143817
95-th percentile26.81904229
Maximum29.89020821
Range29.91420715
Interquartile range (IQR)12.5432653

Descriptive statistics

Standard deviation8.13414365
Coefficient of variation (CV)0.796030109
Kurtosis-0.4615941007
Mean10.21838691
Median Absolute Deviation (MAD)6.531308709
Skewness0.7671342238
Sum12262.0643
Variance66.16429291
MonotonicityNot monotonic
2022-07-31T02:10:06.514092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.6010740611
 
0.1%
2.5904282271
 
0.1%
2.2446866011
 
0.1%
6.638746271
 
0.1%
21.852439311
 
0.1%
0.6070261691
 
0.1%
29.121772331
 
0.1%
3.3709384121
 
0.1%
1.2325292161
 
0.1%
1.8438883981
 
0.1%
Other values (1190)1190
99.2%
ValueCountFrequency (%)
-0.0239989441
0.1%
0.0440093041
0.1%
0.0966608971
0.1%
0.1260198741
0.1%
0.129134721
0.1%
0.1889906241
0.1%
0.2570683771
0.1%
0.2598362941
0.1%
0.3050596821
0.1%
0.3226375141
0.1%
ValueCountFrequency (%)
29.890208211
0.1%
29.7988571
0.1%
29.7722291
0.1%
29.425706351
0.1%
29.391170841
0.1%
29.270605781
0.1%
29.248226271
0.1%
29.223638281
0.1%
29.147260651
0.1%
29.121772331
0.1%

Education
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
447 
4
326 
2
220 
1
174 
5
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row1
5th row4

Common Values

ValueCountFrequency (%)
3447
37.2%
4326
27.2%
2220
18.3%
1174
 
14.5%
533
 
2.8%

Length

2022-07-31T02:10:06.613864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:06.701227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3447
37.2%
4326
27.2%
2220
18.3%
1174
 
14.5%
533
 
2.8%

Most occurring characters

ValueCountFrequency (%)
3447
37.2%
4326
27.2%
2220
18.3%
1174
 
14.5%
533
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3447
37.2%
4326
27.2%
2220
18.3%
1174
 
14.5%
533
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3447
37.2%
4326
27.2%
2220
18.3%
1174
 
14.5%
533
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3447
37.2%
4326
27.2%
2220
18.3%
1174
 
14.5%
533
 
2.8%

EducationField
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Life Sciences
501 
Medical
334 
Marketing
154 
Technical Degree
98 
Other
97 

Length

Max length16
Median length15
Mean length10.44166667
Min length5

Characters and Unicode

Total characters12530
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedical
2nd rowTechnical Degree
3rd rowLife Sciences
4th rowMedical
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences501
41.8%
Medical334
27.8%
Marketing154
 
12.8%
Technical Degree98
 
8.2%
Other97
 
8.1%
Human Resources16
 
1.3%

Length

2022-07-31T02:10:06.794565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:06.899685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
life501
27.6%
sciences501
27.6%
medical334
18.4%
marketing154
 
8.5%
technical98
 
5.4%
degree98
 
5.4%
other97
 
5.3%
human16
 
0.9%
resources16
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e2512
20.0%
i1588
12.7%
c1548
12.4%
n769
 
6.1%
615
 
4.9%
a602
 
4.8%
s533
 
4.3%
L501
 
4.0%
f501
 
4.0%
S501
 
4.0%
Other values (16)2860
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10100
80.6%
Uppercase Letter1815
 
14.5%
Space Separator615
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2512
24.9%
i1588
15.7%
c1548
15.3%
n769
 
7.6%
a602
 
6.0%
s533
 
5.3%
f501
 
5.0%
l432
 
4.3%
r365
 
3.6%
d334
 
3.3%
Other values (7)916
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
L501
27.6%
S501
27.6%
M488
26.9%
T98
 
5.4%
D98
 
5.4%
O97
 
5.3%
H16
 
0.9%
R16
 
0.9%
Space Separator
ValueCountFrequency (%)
615
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11915
95.1%
Common615
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2512
21.1%
i1588
13.3%
c1548
13.0%
n769
 
6.5%
a602
 
5.1%
s533
 
4.5%
L501
 
4.2%
f501
 
4.2%
S501
 
4.2%
M488
 
4.1%
Other values (15)2372
19.9%
Common
ValueCountFrequency (%)
615
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2512
20.0%
i1588
12.7%
c1548
12.4%
n769
 
6.1%
615
 
4.9%
a602
 
4.8%
s533
 
4.3%
L501
 
4.0%
f501
 
4.0%
S501
 
4.0%
Other values (16)2860
22.8%

EmployeeNumber
Real number (ℝ≥0)

Distinct165
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1077.855
Minimum12
Maximum2060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:07.009569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile60.95
Q1699
median1059
Q31591
95-th percentile1862
Maximum2060
Range2048
Interquartile range (IQR)892

Descriptive statistics

Standard deviation580.1854947
Coefficient of variation (CV)0.5382778711
Kurtosis-0.9783382593
Mean1077.855
Median Absolute Deviation (MAD)528
Skewness-0.3038541317
Sum1293426
Variance336615.2083
MonotonicityNot monotonic
2022-07-31T02:10:07.122617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
975106
 
8.8%
158773
 
6.1%
171966
 
5.5%
113862
 
5.2%
159158
 
4.8%
186246
 
3.8%
2839
 
3.2%
69928
 
2.3%
129126
 
2.2%
12022
 
1.8%
Other values (155)674
56.2%
ValueCountFrequency (%)
121
 
0.1%
155
 
0.4%
2839
3.2%
352
 
0.2%
421
 
0.1%
461
 
0.1%
545
 
0.4%
551
 
0.1%
605
 
0.4%
616
 
0.5%
ValueCountFrequency (%)
20605
0.4%
20487
0.6%
20277
0.6%
20223
0.2%
19992
 
0.2%
19963
0.2%
19956
0.5%
19681
 
0.1%
19481
 
0.1%
19292
 
0.2%

EnvironmentSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
4
368 
3
326 
1
275 
2
231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
4368
30.7%
3326
27.2%
1275
22.9%
2231
19.2%

Length

2022-07-31T02:10:07.225853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:07.324766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
4368
30.7%
3326
27.2%
1275
22.9%
2231
19.2%

Most occurring characters

ValueCountFrequency (%)
4368
30.7%
3326
27.2%
1275
22.9%
2231
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4368
30.7%
3326
27.2%
1275
22.9%
2231
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4368
30.7%
3326
27.2%
1275
22.9%
2231
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4368
30.7%
3326
27.2%
1275
22.9%
2231
19.2%

Gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Male
713 
Female
487 

Length

Max length6
Median length4
Mean length4.811666667
Min length4

Characters and Unicode

Total characters5774
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male713
59.4%
Female487
40.6%

Length

2022-07-31T02:10:07.419849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:07.509383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
male713
59.4%
female487
40.6%

Most occurring characters

ValueCountFrequency (%)
e1687
29.2%
a1200
20.8%
l1200
20.8%
M713
12.3%
F487
 
8.4%
m487
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4574
79.2%
Uppercase Letter1200
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1687
36.9%
a1200
26.2%
l1200
26.2%
m487
 
10.6%
Uppercase Letter
ValueCountFrequency (%)
M713
59.4%
F487
40.6%

Most occurring scripts

ValueCountFrequency (%)
Latin5774
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1687
29.2%
a1200
20.8%
l1200
20.8%
M713
12.3%
F487
 
8.4%
m487
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1687
29.2%
a1200
20.8%
l1200
20.8%
M713
12.3%
F487
 
8.4%
m487
 
8.4%

HourlyRate
Real number (ℝ≥0)

Distinct71
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.26166667
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:07.593565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile34
Q148
median67
Q384
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.25780128
Coefficient of variation (CV)0.3057242943
Kurtosis-1.231558873
Mean66.26166667
Median Absolute Deviation (MAD)18
Skewness-0.07053581684
Sum79514
Variance410.3785126
MonotonicityNot monotonic
2022-07-31T02:10:07.698580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4852
 
4.3%
4549
 
4.1%
9841
 
3.4%
8534
 
2.8%
7433
 
2.8%
7933
 
2.8%
7829
 
2.4%
6127
 
2.2%
3026
 
2.2%
5625
 
2.1%
Other values (61)851
70.9%
ValueCountFrequency (%)
3026
2.2%
3111
0.9%
3217
1.4%
334
 
0.3%
347
 
0.6%
355
 
0.4%
368
 
0.7%
3723
1.9%
387
 
0.6%
3922
1.8%
ValueCountFrequency (%)
10010
 
0.8%
995
 
0.4%
9841
3.4%
9724
2.0%
967
 
0.6%
9521
1.8%
9422
1.8%
938
 
0.7%
9221
1.8%
9116
 
1.3%

JobInvolvement
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
709 
2
339 
4
101 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3709
59.1%
2339
28.2%
4101
 
8.4%
151
 
4.2%

Length

2022-07-31T02:10:07.806019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:07.890798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3709
59.1%
2339
28.2%
4101
 
8.4%
151
 
4.2%

Most occurring characters

ValueCountFrequency (%)
3709
59.1%
2339
28.2%
4101
 
8.4%
151
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3709
59.1%
2339
28.2%
4101
 
8.4%
151
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3709
59.1%
2339
28.2%
4101
 
8.4%
151
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3709
59.1%
2339
28.2%
4101
 
8.4%
151
 
4.2%

JobLevel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
1
462 
2
377 
3
172 
4
126 
5
63 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1462
38.5%
2377
31.4%
3172
 
14.3%
4126
 
10.5%
563
 
5.2%

Length

2022-07-31T02:10:07.968729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:08.065228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1462
38.5%
2377
31.4%
3172
 
14.3%
4126
 
10.5%
563
 
5.2%

Most occurring characters

ValueCountFrequency (%)
1462
38.5%
2377
31.4%
3172
 
14.3%
4126
 
10.5%
563
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1462
38.5%
2377
31.4%
3172
 
14.3%
4126
 
10.5%
563
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1462
38.5%
2377
31.4%
3172
 
14.3%
4126
 
10.5%
563
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1462
38.5%
2377
31.4%
3172
 
14.3%
4126
 
10.5%
563
 
5.2%

JobRole
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Research Scientist
252 
Sales Executive
249 
Laboratory Technician
179 
Manufacturing Director
129 
Sales Representative
101 
Other values (4)
290 

Length

Max length25
Median length21
Mean length18.115
Min length7

Characters and Unicode

Total characters21738
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaboratory Technician
2nd rowResearch Scientist
3rd rowHuman Resources
4th rowLaboratory Technician
5th rowManufacturing Director

Common Values

ValueCountFrequency (%)
Research Scientist252
21.0%
Sales Executive249
20.8%
Laboratory Technician179
14.9%
Manufacturing Director129
10.8%
Sales Representative101
8.4%
Healthcare Representative92
 
7.7%
Research Director90
 
7.5%
Manager75
 
6.2%
Human Resources33
 
2.8%

Length

2022-07-31T02:10:08.155587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:08.312591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
sales350
15.1%
research342
14.7%
scientist252
10.8%
executive249
10.7%
director219
9.4%
representative193
8.3%
laboratory179
7.7%
technician179
7.7%
manufacturing129
 
5.5%
healthcare92
 
4.0%
Other values (3)141
6.1%

Most occurring characters

ValueCountFrequency (%)
e3279
15.1%
a2047
 
9.4%
t1758
 
8.1%
c1674
 
7.7%
r1660
 
7.6%
i1652
 
7.6%
s1203
 
5.5%
n1169
 
5.4%
1125
 
5.2%
h613
 
2.8%
Other values (19)5558
25.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18288
84.1%
Uppercase Letter2325
 
10.7%
Space Separator1125
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3279
17.9%
a2047
11.2%
t1758
9.6%
c1674
9.2%
r1660
9.1%
i1652
9.0%
s1203
 
6.6%
n1169
 
6.4%
h613
 
3.4%
o610
 
3.3%
Other values (10)2623
14.3%
Uppercase Letter
ValueCountFrequency (%)
S602
25.9%
R568
24.4%
E249
10.7%
D219
 
9.4%
M204
 
8.8%
L179
 
7.7%
T179
 
7.7%
H125
 
5.4%
Space Separator
ValueCountFrequency (%)
1125
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin20613
94.8%
Common1125
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3279
15.9%
a2047
9.9%
t1758
 
8.5%
c1674
 
8.1%
r1660
 
8.1%
i1652
 
8.0%
s1203
 
5.8%
n1169
 
5.7%
h613
 
3.0%
o610
 
3.0%
Other values (18)4948
24.0%
Common
ValueCountFrequency (%)
1125
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII21738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3279
15.1%
a2047
 
9.4%
t1758
 
8.1%
c1674
 
7.7%
r1660
 
7.6%
i1652
 
7.6%
s1203
 
5.5%
n1169
 
5.4%
1125
 
5.2%
h613
 
2.8%
Other values (19)5558
25.6%

JobSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
371 
4
361 
2
236 
1
232 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3371
30.9%
4361
30.1%
2236
19.7%
1232
19.3%

Length

2022-07-31T02:10:08.420618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:08.508432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3371
30.9%
4361
30.1%
2236
19.7%
1232
19.3%

Most occurring characters

ValueCountFrequency (%)
3371
30.9%
4361
30.1%
2236
19.7%
1232
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3371
30.9%
4361
30.1%
2236
19.7%
1232
19.3%

Most occurring scripts

ValueCountFrequency (%)
Common1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3371
30.9%
4361
30.1%
2236
19.7%
1232
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3371
30.9%
4361
30.1%
2236
19.7%
1232
19.3%

MaritalStatus
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Married
578 
Single
359 
Divorced
263 

Length

Max length8
Median length7
Mean length6.92
Min length6

Characters and Unicode

Total characters8304
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowDivorced
3rd rowMarried
4th rowDivorced
5th rowDivorced

Common Values

ValueCountFrequency (%)
Married578
48.2%
Single359
29.9%
Divorced263
21.9%

Length

2022-07-31T02:10:08.595635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:08.692906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
married578
48.2%
single359
29.9%
divorced263
21.9%

Most occurring characters

ValueCountFrequency (%)
r1419
17.1%
i1200
14.5%
e1200
14.5%
d841
10.1%
M578
7.0%
a578
7.0%
S359
 
4.3%
n359
 
4.3%
g359
 
4.3%
l359
 
4.3%
Other values (4)1052
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7104
85.5%
Uppercase Letter1200
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r1419
20.0%
i1200
16.9%
e1200
16.9%
d841
11.8%
a578
8.1%
n359
 
5.1%
g359
 
5.1%
l359
 
5.1%
v263
 
3.7%
o263
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
M578
48.2%
S359
29.9%
D263
21.9%

Most occurring scripts

ValueCountFrequency (%)
Latin8304
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r1419
17.1%
i1200
14.5%
e1200
14.5%
d841
10.1%
M578
7.0%
a578
7.0%
S359
 
4.3%
n359
 
4.3%
g359
 
4.3%
l359
 
4.3%
Other values (4)1052
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII8304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r1419
17.1%
i1200
14.5%
e1200
14.5%
d841
10.1%
M578
7.0%
a578
7.0%
S359
 
4.3%
n359
 
4.3%
g359
 
4.3%
l359
 
4.3%
Other values (4)1052
12.7%

MonthlyIncome
Real number (ℝ≥0)

Distinct188
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7052.521667
Minimum1052
Maximum19833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:08.784402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1052
5-th percentile2088
Q13537
median5071
Q38715.25
95-th percentile17159
Maximum19833
Range18781
Interquartile range (IQR)5178.25

Descriptive statistics

Standard deviation5033.677026
Coefficient of variation (CV)0.7137414479
Kurtosis0.08854611116
Mean7052.521667
Median Absolute Deviation (MAD)2121
Skewness1.167268396
Sum8463026
Variance25337904.4
MonotonicityNot monotonic
2022-07-31T02:10:08.895537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
478964
 
5.3%
1715943
 
3.6%
493636
 
3.0%
745734
 
2.8%
418933
 
2.8%
522829
 
2.4%
201129
 
2.4%
507126
 
2.2%
1903326
 
2.2%
1324725
 
2.1%
Other values (178)855
71.2%
ValueCountFrequency (%)
10521
 
0.1%
11184
 
0.3%
12611
 
0.1%
13591
 
0.1%
15636
 
0.5%
19511
 
0.1%
20071
 
0.1%
20082
 
0.2%
201129
2.4%
20244
 
0.3%
ValueCountFrequency (%)
198331
 
0.1%
1950214
 
1.2%
1903326
2.2%
188245
 
0.4%
187114
 
0.3%
176503
 
0.2%
174443
 
0.2%
171692
 
0.2%
1715943
3.6%
171236
 
0.5%

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.614166667
Minimum0
Maximum9
Zeros167
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:09.166310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.460422193
Coefficient of variation (CV)0.9411879605
Kurtosis-0.05975002378
Mean2.614166667
Median Absolute Deviation (MAD)1
Skewness1.03469726
Sum3137
Variance6.05367737
MonotonicityNot monotonic
2022-07-31T02:10:09.238955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1438
36.5%
0167
 
13.9%
3145
 
12.1%
2117
 
9.8%
487
 
7.2%
667
 
5.6%
863
 
5.2%
758
 
4.8%
538
 
3.2%
920
 
1.7%
ValueCountFrequency (%)
0167
 
13.9%
1438
36.5%
2117
 
9.8%
3145
 
12.1%
487
 
7.2%
538
 
3.2%
667
 
5.6%
758
 
4.8%
863
 
5.2%
920
 
1.7%
ValueCountFrequency (%)
920
 
1.7%
863
 
5.2%
758
 
4.8%
667
 
5.6%
538
 
3.2%
487
 
7.2%
3145
 
12.1%
2117
 
9.8%
1438
36.5%
0167
 
13.9%

Over18
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
True
1200 
ValueCountFrequency (%)
True1200
100.0%
2022-07-31T02:10:09.343551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

OverTime
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
882 
True
318 
ValueCountFrequency (%)
False882
73.5%
True318
 
26.5%
2022-07-31T02:10:09.435133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.115
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:09.503276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.632414766
Coefficient of variation (CV)0.2403185422
Kurtosis-0.4465328334
Mean15.115
Median Absolute Deviation (MAD)2
Skewness0.7896222104
Sum18138
Variance13.19443703
MonotonicityNot monotonic
2022-07-31T02:10:09.581467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
13185
15.4%
12182
15.2%
11173
14.4%
14161
13.4%
1983
6.9%
1876
6.3%
1667
 
5.6%
2058
 
4.8%
1754
 
4.5%
2251
 
4.2%
Other values (5)110
9.2%
ValueCountFrequency (%)
11173
14.4%
12182
15.2%
13185
15.4%
14161
13.4%
1544
 
3.7%
1667
 
5.6%
1754
 
4.5%
1876
6.3%
1983
6.9%
2058
 
4.8%
ValueCountFrequency (%)
258
 
0.7%
2413
 
1.1%
2329
 
2.4%
2251
4.2%
2116
 
1.3%
2058
4.8%
1983
6.9%
1876
6.3%
1754
4.5%
1667
5.6%

PerformanceRating
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
1068 
4
132 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
31068
89.0%
4132
 
11.0%

Length

2022-07-31T02:10:09.671348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:09.756774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
31068
89.0%
4132
 
11.0%

Most occurring characters

ValueCountFrequency (%)
31068
89.0%
4132
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
31068
89.0%
4132
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
31068
89.0%
4132
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31068
89.0%
4132
 
11.0%

RelationshipSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
380 
4
300 
2
280 
1
240 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row4
5th row2

Common Values

ValueCountFrequency (%)
3380
31.7%
4300
25.0%
2280
23.3%
1240
20.0%

Length

2022-07-31T02:10:09.832838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:09.918986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3380
31.7%
4300
25.0%
2280
23.3%
1240
20.0%

Most occurring characters

ValueCountFrequency (%)
3380
31.7%
4300
25.0%
2280
23.3%
1240
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3380
31.7%
4300
25.0%
2280
23.3%
1240
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3380
31.7%
4300
25.0%
2280
23.3%
1240
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3380
31.7%
4300
25.0%
2280
23.3%
1240
20.0%

StandardHours
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
80
1200 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80
2nd row80
3rd row80
4th row80
5th row80

Common Values

ValueCountFrequency (%)
801200
100.0%

Length

2022-07-31T02:10:10.010481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:10.119526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
801200
100.0%

Most occurring characters

ValueCountFrequency (%)
81200
50.0%
01200
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
81200
50.0%
01200
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common2400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
81200
50.0%
01200
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
81200
50.0%
01200
50.0%

StockOptionLevel
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0
480 
1
476 
2
172 
3
72 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0480
40.0%
1476
39.7%
2172
 
14.3%
372
 
6.0%

Length

2022-07-31T02:10:10.246059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:10.363127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0480
40.0%
1476
39.7%
2172
 
14.3%
372
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0480
40.0%
1476
39.7%
2172
 
14.3%
372
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0480
40.0%
1476
39.7%
2172
 
14.3%
372
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0480
40.0%
1476
39.7%
2172
 
14.3%
372
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0480
40.0%
1476
39.7%
2172
 
14.3%
372
 
6.0%

TotalWorkingYears
Real number (ℝ≥0)

HIGH CORRELATION

Distinct36
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.63166667
Minimum0
Maximum36
Zeros7
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:10.518525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median9
Q315
95-th percentile26
Maximum36
Range36
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.721334928
Coefficient of variation (CV)0.7262581841
Kurtosis0.5515335154
Mean10.63166667
Median Absolute Deviation (MAD)4
Skewness1.022273194
Sum12758
Variance59.61901307
MonotonicityNot monotonic
2022-07-31T02:10:10.647660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
10189
15.8%
6120
 
10.0%
197
 
8.1%
967
 
5.6%
464
 
5.3%
562
 
5.2%
1654
 
4.5%
853
 
4.4%
250
 
4.2%
743
 
3.6%
Other values (26)401
33.4%
ValueCountFrequency (%)
07
 
0.6%
197
8.1%
250
4.2%
339
 
3.2%
464
5.3%
562
5.2%
6120
10.0%
743
 
3.6%
853
4.4%
967
5.6%
ValueCountFrequency (%)
362
 
0.2%
341
 
0.1%
3311
0.9%
329
0.8%
3120
1.7%
301
 
0.1%
295
 
0.4%
282
 
0.2%
274
 
0.3%
2614
1.2%

TrainingTimesLastYear
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.755
Minimum0
Maximum6
Zeros53
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:10.799064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.256167437
Coefficient of variation (CV)0.4559591424
Kurtosis0.5001608644
Mean2.755
Median Absolute Deviation (MAD)1
Skewness0.3962815218
Sum3306
Variance1.577956631
MonotonicityNot monotonic
2022-07-31T02:10:10.901721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2435
36.2%
3411
34.2%
4109
 
9.1%
5100
 
8.3%
157
 
4.8%
053
 
4.4%
635
 
2.9%
ValueCountFrequency (%)
053
 
4.4%
157
 
4.8%
2435
36.2%
3411
34.2%
4109
 
9.1%
5100
 
8.3%
635
 
2.9%
ValueCountFrequency (%)
635
 
2.9%
5100
 
8.3%
4109
 
9.1%
3411
34.2%
2435
36.2%
157
 
4.8%
053
 
4.4%

WorkLifeBalance
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3
707 
2
323 
4
101 
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3707
58.9%
2323
26.9%
4101
 
8.4%
169
 
5.8%

Length

2022-07-31T02:10:11.011577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:11.114970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3707
58.9%
2323
26.9%
4101
 
8.4%
169
 
5.8%

Most occurring characters

ValueCountFrequency (%)
3707
58.9%
2323
26.9%
4101
 
8.4%
169
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3707
58.9%
2323
26.9%
4101
 
8.4%
169
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3707
58.9%
2323
26.9%
4101
 
8.4%
169
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3707
58.9%
2323
26.9%
4101
 
8.4%
169
 
5.8%

YearsAtCompany
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.351666667
Minimum0
Maximum32
Zeros23
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:11.204854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q39
95-th percentile18
Maximum32
Range32
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.120531915
Coefficient of variation (CV)0.8061713852
Kurtosis2.459739693
Mean6.351666667
Median Absolute Deviation (MAD)3
Skewness1.409076389
Sum7622
Variance26.21984709
MonotonicityNot monotonic
2022-07-31T02:10:11.296580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1164
13.7%
5164
13.7%
2126
10.5%
10118
9.8%
3110
9.2%
679
6.6%
477
 
6.4%
964
 
5.3%
753
 
4.4%
850
 
4.2%
Other values (17)195
16.2%
ValueCountFrequency (%)
023
 
1.9%
1164
13.7%
2126
10.5%
3110
9.2%
477
6.4%
5164
13.7%
679
6.6%
753
 
4.4%
850
 
4.2%
964
 
5.3%
ValueCountFrequency (%)
321
 
0.1%
312
 
0.2%
264
 
0.3%
253
 
0.2%
222
 
0.2%
2112
1.0%
2022
1.8%
198
 
0.7%
187
 
0.6%
175
 
0.4%

YearsInCurrentRole
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.249166667
Minimum0
Maximum17
Zeros208
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:11.387461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.688507405
Coefficient of variation (CV)0.8680543021
Kurtosis-0.2906384023
Mean4.249166667
Median Absolute Deviation (MAD)3
Skewness0.7637755119
Sum5099
Variance13.60508688
MonotonicityNot monotonic
2022-07-31T02:10:11.470063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2329
27.4%
0208
17.3%
7135
11.2%
391
 
7.6%
977
 
6.4%
477
 
6.4%
874
 
6.2%
1050
 
4.2%
146
 
3.8%
628
 
2.3%
Other values (8)85
 
7.1%
ValueCountFrequency (%)
0208
17.3%
146
 
3.8%
2329
27.4%
391
 
7.6%
477
 
6.4%
521
 
1.8%
628
 
2.3%
7135
11.2%
874
 
6.2%
977
 
6.4%
ValueCountFrequency (%)
171
 
0.1%
164
 
0.3%
156
 
0.5%
143
 
0.2%
1320
 
1.7%
128
 
0.7%
1122
 
1.8%
1050
4.2%
977
6.4%
874
6.2%

YearsSinceLastPromotion
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.163333333
Minimum0
Maximum15
Zeros499
Zeros (%)41.6%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:11.554912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile11
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.312219481
Coefficient of variation (CV)1.531072179
Kurtosis3.06031401
Mean2.163333333
Median Absolute Deviation (MAD)1
Skewness1.922999513
Sum2596
Variance10.97079789
MonotonicityNot monotonic
2022-07-31T02:10:11.654115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0499
41.6%
1305
25.4%
2121
 
10.1%
754
 
4.5%
545
 
3.8%
836
 
3.0%
1134
 
2.8%
326
 
2.2%
421
 
1.8%
615
 
1.2%
Other values (6)44
 
3.7%
ValueCountFrequency (%)
0499
41.6%
1305
25.4%
2121
 
10.1%
326
 
2.2%
421
 
1.8%
545
 
3.8%
615
 
1.2%
754
 
4.5%
836
 
3.0%
911
 
0.9%
ValueCountFrequency (%)
1512
 
1.0%
143
 
0.2%
137
 
0.6%
125
 
0.4%
1134
2.8%
106
 
0.5%
911
 
0.9%
836
3.0%
754
4.5%
615
 
1.2%

YearsWithCurrManager
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.78
Minimum0
Maximum17
Zeros239
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-07-31T02:10:11.745773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile9
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.314224908
Coefficient of variation (CV)0.8767790761
Kurtosis-0.2729006989
Mean3.78
Median Absolute Deviation (MAD)3
Skewness0.7541176331
Sum4536
Variance10.98408674
MonotonicityNot monotonic
2022-07-31T02:10:11.822763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2306
25.5%
0239
19.9%
7154
12.8%
3124
10.3%
895
 
7.9%
489
 
7.4%
971
 
5.9%
152
 
4.3%
1116
 
1.3%
514
 
1.2%
Other values (6)40
 
3.3%
ValueCountFrequency (%)
0239
19.9%
152
 
4.3%
2306
25.5%
3124
10.3%
489
 
7.4%
514
 
1.2%
611
 
0.9%
7154
12.8%
895
 
7.9%
971
 
5.9%
ValueCountFrequency (%)
172
 
0.2%
141
 
0.1%
1311
 
0.9%
127
 
0.6%
1116
 
1.3%
108
 
0.7%
971
5.9%
895
7.9%
7154
12.8%
611
 
0.9%

Attrition
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0
987 
1
213 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0987
82.2%
1213
 
17.8%

Length

2022-07-31T02:10:11.916150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T02:10:11.999359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0987
82.2%
1213
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0987
82.2%
1213
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0987
82.2%
1213
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0987
82.2%
1213
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0987
82.2%
1213
 
17.8%

Interactions

2022-07-31T02:10:02.059742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:31.919815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:33.957185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:36.119573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:38.535571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:40.243120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:41.909888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:44.979660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:47.690235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:50.151018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:51.993335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:54.006161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:55.799487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:58.016217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:00.140938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:02.166862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:32.102571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:34.088406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:36.285614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:38.658847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:40.353809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:42.093689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:45.106946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:47.842765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:50.261653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:52.132434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:54.120844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:55.972101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:58.133610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:00.296156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:02.273400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:32.232929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:34.195229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:36.468715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:38.770212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:40.459105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:42.658360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:45.219368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:48.038265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:50.385618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:52.256298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:54.232142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:56.140352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:58.277799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:00.431178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:02.381346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:32.351492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:34.297426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:36.629226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:38.875785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:40.575348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:43.048434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:45.367214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:48.207533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:50.512333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:52.360050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:54.341747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:56.285039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:58.410638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:00.545807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:02.525214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:32.470708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:34.426105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:36.799442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:38.987845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:40.690922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:43.266208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:45.769609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:48.400560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:50.613779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:52.460598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:54.440811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:56.422204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:58.568977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:00.696453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:02.683800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:32.595599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:34.551913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:36.917306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:39.090790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:40.789201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:43.386956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:45.931865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:48.757492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:50.710952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:52.553473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:54.532042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:56.519314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:58.663707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:00.824807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:02.842456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:32.735177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:34.663851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:37.035642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:39.197387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:40.893151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:43.535386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:46.080575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:48.936896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:50.821687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:52.653169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:54.634276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:56.648750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:58.777560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:00.934501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:03.013386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:32.852384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:34.820606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:37.149982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:39.299666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:41.002553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:43.674186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:46.231204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:49.206100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:50.943599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:52.769018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:54.739868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:56.803793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:58.937011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:01.041782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:03.129313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:32.955941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:34.929198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:37.263217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:39.414488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:41.104882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:43.846744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:46.422160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:49.320582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:51.136894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:52.894061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:54.834193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:56.902008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:59.060324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:01.138697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:03.245242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:33.081645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:35.101871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:37.460136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:39.537609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:41.208331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:44.036063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:46.618308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:49.421835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:51.311372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:53.201985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:54.953907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:57.052774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:59.221187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:01.243187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:03.352344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:33.351636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:35.240734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:37.616065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:39.650433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:41.314742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:44.168160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:46.791222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:49.523511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:51.410759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:53.303838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:55.053409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:57.185131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:59.336833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:01.348802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:03.455883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:33.463577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:35.395987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:37.740258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:39.756113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:41.417991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:44.282471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:46.982778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:49.622515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:51.518060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:53.405011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:55.182524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:57.319866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:59.479326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:01.455960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:03.562870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:33.568854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:35.519197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:37.887172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:39.858501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:41.515616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:44.420567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:47.150316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:49.719520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:51.645927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:53.504081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:55.334422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:57.638432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:59.615784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:01.563214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:03.671152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:33.700918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:35.703419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:38.281683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:39.980822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:41.621030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:44.565251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:47.346168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:49.849925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:51.766838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:53.751435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:55.479837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:57.736871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:59.776506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:01.668169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:03.781148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:33.807820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:35.872622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:38.391202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:40.110771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:41.774177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:44.754760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:47.511143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:50.047858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:51.874502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:53.903042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:55.623450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:57.878829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:09:59.988703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-31T02:10:01.780193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-31T02:10:12.283928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-31T02:10:12.531715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-31T02:10:12.808079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-31T02:10:13.111071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-31T02:10:13.376742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-31T02:10:03.997720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-31T02:10:04.758329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idAgeBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttrition
0026Travel_Rarely450.941476Research & Development7.6010743Medical12914Male4321Laboratory Technician2Single163071YNo1333800132118700
1347Travel_Rarely730.235896Research & Development26.7394893Technical Degree15872Male9821Research Scientist1Divorced95268YYes1133801201159440
2726Travel_Rarely1082.560066Human Resources7.3747393Life Sciences15912Male8421Human Resources2Married105968YNo183280143332020
31046Travel_Rarely706.247579Research & Development14.7913731Medical15721Female7921Laboratory Technician3Divorced57620YYes204480160154771
41125Travel_Rarely500.610860Research & Development2.1469664Medical9812Male9832Manufacturing Director3Divorced170686YYes143280183310000
51236Non-Travel1025.521404Human Resources1.6895703Life Sciences10271Male4835Research Scientist2Divorced52282YNo163380122332220
61525Travel_Rarely810.789599Research & Development8.4558521Life Sciences14204Female7631Research Scientist4Married64341YYes1134801102353080
72037Travel_Frequently672.328336Research & Development28.5935545Medical6354Male3432Sales Executive3Married49361YNo2042802173432030
82137Travel_Rarely350.271536Sales25.3283353Technical Degree17982Female5712Sales Executive2Single20118YYes224380063332201
92317Travel_Rarely517.111386Sales6.3429312Marketing283Male8521Sales Representative3Single64341YYes243180042322021

Last rows

idAgeBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttrition
1190197646Travel_Rarely390.878064Sales2.5159965Technical Degree1202Male6432Sales Executive2Married56771YNo2232800113398030
1191198018Travel_Rarely867.979366Sales7.4864534Medical16691Male4831Sales Representative3Married55614YYes193180142212001
1192198418Travel_Rarely763.515135Sales6.2288642Medical11381Male4521Sales Representative1Divorced23761YNo133480012252121
1193198634Travel_Rarely1025.887963Sales2.0755204Marketing2073Female3032Sales Executive1Divorced67810YNo1933800102367170
1194198737Non-Travel416.576646Research & Development1.6609323Medical10713Male7932Research Director3Single47891YNo173280053353130
1195198849Travel_Rarely969.251891Research & Development13.2435792Medical15874Male6735Research Director4Married50988YNo1832800333232120
1196199437Travel_Frequently437.940367Research & Development2.3394381Other1374Male8232Manufacturing Director4Married64341YYes113480065292080
1197199640Non-Travel978.883360Human Resources10.2149793Life Sciences15873Male4023Healthcare Representative3Divorced33391YNo12328011923149990
1198199737Travel_Frequently170.494984Sales2.6398792Marketing1201Male9342Sales Executive4Divorced20110YNo163180265332030
1199199826Travel_Rarely72.733977Research & Development28.0160882Life Sciences2151Female9722Healthcare Representative2Divorced47892YNo123180172222221